Abstract
Bioelectrochemical systems (BESs) have emerged as one innovative technology platter where different configurations of the experimental setup provide various applications, ranging from electricity generation, wastewater treatment, hydrogen and utility chemical production, bioremediation, desalination, etc. With growing interests in BESs, the number of mathematical modeling studies in this field has also grown. However, given the complex interdependence of the different processes, conventional deterministic models have so far achieved only moderate success. Considering the large number of unknowns in the system, data-based artificial intelligence (AI) methods, which do not require prior knowledge of the process, are now being used to study BES. This chapter presents a brief description and an overview of the different AI methods that have been used for predicting and controlling the performance of different types of BES.